Monitoring Global Croplands with Coarse Resolution Earth Observations: The Global Agriculture Monitoring (GLAM) Project

In recent years there has been a dramatic increase in the demand for timely, comprehensive global agricultural intelligence. Timely information on global crop production is indispensable for combating the growing stress on the world’s crop production and for securing both short-term and long-term stable and reliable supply of food. Global agriculture monitoring systems are critical to providing this kind of intelligence and global earth observations are an essential component of an effective global agricultural monitoring system as they offer timely, objective, global information on croplands distribution, crop development and conditions as the growing season progresses. The Global Agriculture Monitoring Project (GLAM), a joint NASA, USDA, UMD and SDSU initiative, has built a global agricultural monitoring system that provides the USDA Foreign Agricultural Service (FAS) with timely, easily accessible, scientifically-validated remotely-sensed data and derived products as well as data analysis tools, for crop-condition monitoring and production assessment. This system is an integral component of the USDA’s FAS Decision Support System (DSS) for agriculture. It has significantly improved the FAS crop analysts’ ability to monitor crop conditions, and to quantitatively forecast crop yields through the provision of timely, high-quality global earth observations data in a format customized for FAS alongside a suite of data analysis tools. FAS crop analysts use these satellite data in a ‘convergence of evidence’ approach with meteorological data, field reports, crop models, attaché reports and local reports. The USDA FAS is currently the only operational provider of timely, objective crop production forecasts at the global scale. These forecasts are routinely used by the other US Federal government agencies as well as by commodity trading companies, farmers, relief agencies and foreign governments. This paper discusses the operational components and new developments of the GLAM monitoring system as well as the future role of earth observations in global agricultural monitoring

Contrasting snow and ice albedos derived from MODIS, Landsat ETM+ and airborne data from Langjökull, Iceland

This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.rse.2015.12.051Surface albedo is a key parameter in the energy balance of glaciers and ice sheets because it controls the shortwave radiation budget, which is often the dominant term of a glacier's surface energy balance. Monitoring surface albedo is a key application of remote sensing and achieving consistency between instruments is crucial to accurate assessment of changing albedo. Here we take advantage of a high resolution (5 m) airborne multispectral dataset that was collected over Langj?kull, Iceland in 2007, and compare it with near contemporaneous ETM+ and MODIS imagery. All three radiance datasets are converted to reflectance by applying commonly used atmospheric correction schemes: 6S and FLAASH. These are used to derive broadband albedos. We first assess the similarity of albedo values produced by different atmospheric correction schemes for the same instrument, then contrast results from different instruments. In this way we are able to evaluate the consistency of the available atmospheric correction algorithms and to consider the impacts of different spatial resolutions. We observe that FLAASH leads to the derivation of surface albedos greater than when 6S is used. Albedo is shown to be highly variable at small spatial scales. This leads to consistent differences associated with specific facies types between different resolution instruments, in part attributable to different surface bi-directional reflectance distribution functions. Uncertainties, however, still exist in this analysis as no correction for variable bi-directional reflectance distribution functions could be implemented for the ETM+ and airborne datasets.This work was supported by the UK NERC ARSF ? Project IPY07-08. E. Pope was supported by the NERC Arctic Research Programme under project NE/K00008Xs/1. A. Pope was supported by the National Science Foundation Graduate Research Fellowship Programme under Grant No. DGE-1038596 and by Trinity College, Cambridge. E. Miles was supported by a Gates Cambridge Scholarship and by Trinity College, Cambridge. Fieldwork associated with the ATM flights was supported by grants from the University of Cambridge Scandinavian Studies Fund and the B. B. Roberts Fund. Finally, we would also like to thank NASA LP DAAC for the freely available MODIS and Landsat data used.